README.md

---
license: apache-2.0
language:
- ta
---

# **Debug Divas: Colloquial Tamil Translation Model**
🚀 **Fine-tuned Mistral-7B for English-to-Colloquial Tamil Translation**  

![Hugging Face](https://img.shields.io/badge/HuggingFace-Model-yellow?style=flat)  
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)  

## 🌟 **Overview**
This model is a **fine-tuned version of Mistral-7B** using **Unsloth's FastLanguageModel**, designed specifically to translate **English text into colloquial Tamil** (spoken Tamil). It is optimized for **real-world Tamil conversations**, making it useful for chatbots, assistants, and translation tools.

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## 📖 **Model Details**
- **Base Model**: [Mistral-7B-Instruct](https://huggingface.co/mistral-7b-instruct)
- **Fine-Tuned Dataset**: Custom dataset (`debug_divas_dataset.json`) with **English → Colloquial Tamil** translation pairs.
- **Training Library**: [Unsloth](https://github.com/unslothai/unsloth) (optimized training for large models)
- **Max Sequence Length**: 128 tokens  
- **Batch Size**: 8  
- **Epochs**: 3  
- **Optimizer**: AdamW  

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## 🔧 **Installation & Setup**
To use this model, install the necessary dependencies:

```bash
pip install torch transformers datasets unsloth accelerate
```

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## 🚀 **Usage**
### **Load Model & Tokenizer**
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load fine-tuned model from Hugging Face
model_name = "your-huggingface-username/debug-divas-tamil-translation"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Translation function
def translate_english_to_tamil(input_text):
    instruction = "Translate the following English sentence to colloquial Tamil"
    inputs = tokenizer(f"{instruction}: {input_text}", return_tensors="pt").to(device)
    
    translated_tokens = model.generate(**inputs, max_length=128)
    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
    
    return translated_text

# Example usage
input_text = "The pharmacy is near the bus stop."
translated_text = translate_english_to_tamil(input_text)
print("Colloquial Tamil:", translated_text)
```

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## 📝 **Example Outputs**
| **English** | **Colloquial Tamil Translation** |
|------------|---------------------------------|
| "How are you?" | "நீங்க எப்படி இருக்கீங்க?" |
| "I am going to the market." | "நான் மார்க்கெட்டுக்கு பொறேன்." |
| "The pharmacy is near the bus stop." | "மருந்துக் கடை பஸ்ஸ்டாப் அருகே இருக்க." |

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## 📚 **Dataset**
The dataset contains **pairs of English sentences** with their **colloquial Tamil translations**.  
Example format:

```json
[
  {
    "input": "How are you?",
    "output": "நீங்க எப்படி இருக்கீங்க?"
  },
  {
    "input": "I am going to the market.",
    "output": "நான் மார்க்கெட்டுக்கு பொறேன்."
  }
]
```

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## 🏗 **Training Details**
The model was fine-tuned using **UnslothTrainer** with the following hyperparameters:

- **Batch Size**: 8  
- **Epochs**: 3  
- **Learning Rate**: 2e-5  
- **FP16 Training**: Disabled  
- **Optimizer**: AdamW  
- **Dataset Split**: 80% Train, 20% Test  



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## ⚖ **License & Citation**
This model is released under the **MIT License**. If you use it in your work, please cite:

```bibtex
@misc{debugdivas2025,
  author = {Debug Divas},
  title = {Fine-tuned Mistral-7B for Colloquial Tamil Translation},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/your-huggingface-username/debug-divas-tamil-translation}
}
```

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Dataset Linke : https://huggingface.co/datasets/anitha2520/debug_divas45/tree/main
anitha2520/debug_divas45

## ❤️ **Contributions & Feedback**
We welcome feedback and contributions! Feel free to open an issue or contribute to our dataset.  

📧 **Contact:** [xidanitha@gmail.com]  

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